BUSINESS ANALYTICS AND IMPLICATIONS FOR APPLIED STATISTICS EDUCATION Sam Woolford, Bentley University 2016 Joint Statistical Meetings Chicago, IL August 4, JSM Chicago, IL1

OUTLINE  Defining Business Analytics  Historical Context  Business Analytics Requirements  Implications for Applied Statistics Education  Discussion August 4, JSM Chicago, IL

WHAT IS BUSINESS ANALYTICS?  Is it  Applied statistics?  Data science?  Statistical engineering?  Machine learning?  Operations research?  Who had heard of Business Analytics ten years ago? August 4, JSM Chicago, IL

WHAT IS BUSINESS ANALYTICS August 4,  Wikipedia “Business analytics makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling …to drive decision making.”  Translation (from a business perspective)  What happened?  Why is this happening?  What if the trend continues?  What will happen next?  What is the best that can happen? 2016 JSM Chicago, IL

HISTORICAL CONTEXT  Analytics has a long history in business  Frederick Taylor in the 18 th century  Quality Management Focus on process and customer  Reengineering Added a systems component to attain higher performance  Enterprise data management ERP and CRM systems August 4, JSM Chicago, IL

HISTORICAL CONTEXT  Data, Data, Data  Traditional structured data  New unstructured data  Big data  Business issues  Complexity of global business  Compressed decision time frames  The HIPPO is dead August 4, Business Analytics 2016 JSM Chicago, IL

REQUIREMENTS FOR BUSINESS ANALYTICS  Problem definition  Requires business and context knowledge  Requires quantitative frameworks  Problem complexity  Interdisciplinary  Multiple stakeholders  Requires multiple interconnected analyses  Messy data doesn’t conform to assumptions  Unknown sources of variation August 4, JSM Chicago, IL

REQUIREMENTS FOR BUSINESS ANALYTICS  Analytics frameworks  Also multidisciplinary  Belts and suspenders  Ancillary issues  Team and project management  Communications  Innovation and creativity August 4, JSM Chicago, IL

IMPLICATIONS FOR MASTERS PROGRAMS  Typical MS in Applied Stat  Courses (30-33 hours/10-11 courses) Background courses (Probability and Math Stat) Statistical Methods (Linear models, Multivariate) Statistical computing/data management Topic areas (time series, DOE, Bayesian analysis, stochastic processes) Machine learning Big Data  Duration (1.5 to 2 yrs) Shorter time frames being driven from students and competitive pressures August 4, JSM Chicago, IL

IMPLICATIONS FOR MASTERS PROGRAMS  Ancillary skill requirements  Business understanding is important  Capabilities beyond statistical methodology  Multiple analyses required for complex problems  New career paths for applied statisticians  Data Scientist, Chief Data Officer, VP Big Data, Chief Analytics Officer, Director Decision Science  Analytical career paths may more closely parallel existing corporate career paths August 4, JSM Chicago, IL

IMPLICATIONS FOR MASTERS PROGRAMS  No one ‘owns’ the discipline  ASA  INFORMS  Others (big data)  Issues for statistics departments  Lack of business orientation  Methodology oriented as opposed to case oriented  Requires coordination with other departments  Opportunity to enhance stature and relevance of statistics August 4, JSM Chicago, IL

Discussion Contact: August 4, JSM Chicago, IL